Method, electronic device, and computer program product for data processing

ABSTRACT

Embodiments of the present disclosure provide a method, an electronic device, and a computer program product for data processing. The method may include receiving a data conversion strategy from a server. The method may further include determining, in response to receiving unstructured data from a field device, metadata of the unstructured data based on the received data conversion strategy to form a set of metadata. In addition, the method may include transmitting at least a part of the set of metadata to the server. According to embodiments of the present disclosure, edge computing can be performed on unstructured data, which not only enables timely processing of monitoring data, but also reduces computing load on the side of the server, thereby improving the user experience.

RELATED APPLICATION(S)

The present application claims priority to Chinese Patent ApplicationNo. 202210658237.3, filed Jun. 10, 2022, and entitled “Method,Electronic Device, and Computer Program Product for Data Processing,”which is incorporated by reference herein in its entirety.

FIELD

Embodiments of the present disclosure relate to the field of computers,and more particularly, to a method, an electronic device, and a computerprogram product for data processing.

BACKGROUND

With the rise of edge computing and 5G technologies, increasingly moredevices with powerful computing capabilities can be deployed on edgenodes. For example, a smart camera with an embedded custom function(e.g., an automatic vehicle detection function) may exist in a trafficmonitoring scenario. Video data captured by the smart camera may begenerally stored on edge nodes temporarily. It is generally laboriousand time-consuming to search such unstructured data for specific data.How to search the unstructured data and perform other operations thereonis an urgent problem to be solved at present.

SUMMARY

Embodiments of the present disclosure provide a solution for dataprocessing.

In a first aspect of the present disclosure, a method for dataprocessing is provided. The method may include receiving a dataconversion strategy from a server. The method may further includedetermining, in response to receiving unstructured data from a fielddevice, metadata of the unstructured data based on the received dataconversion strategy to form a set of metadata. In addition, the methodmay include transmitting at least a part of the set of metadata to theserver.

In a second aspect of the present disclosure, a method for dataprocessing is provided. The method may include transmitting a dataconversion strategy to one or a plurality of edge computing nodes, thedata conversion strategy being used for converting unstructured datainto metadata. The method may further include receiving, from the one orplurality of edge computing nodes, a set of metadata determined based onthe data conversion strategy. In addition, the method may includedetermining a data processing result at least based on the received setof metadata.

In a third aspect of the present disclosure, an electronic device isprovided, including: a processor; and a memory coupled to the processorand having instructions stored therein, wherein the instructions, whenexecuted by the processor, cause the electronic device to performactions including: receiving a data conversion strategy from a server;determining, in response to receiving unstructured data from a fielddevice, metadata of the unstructured data based on the received dataconversion strategy to form a set of metadata; and transmitting at leasta part of the set of metadata to the server.

In a fourth aspect of the present disclosure, an electronic device isprovided, which includes a processor; and a memory coupled to theprocessor and having instructions stored therein, wherein theinstructions, when executed by the processor, cause the electronicdevice to perform actions including: transmitting a data conversionstrategy to one or a plurality of edge computing nodes, the dataconversion strategy being used for converting unstructured data intometadata; receiving, from the one or plurality of edge computing nodes,a set of metadata determined based on the data conversion strategy; anddetermining a data processing result at least based on the received setof metadata.

In a fifth aspect of the present disclosure, a computer program productis provided. The computer program product is tangibly stored on anon-transitory computer-readable medium and includes machine-executableinstructions, wherein the machine-executable instructions, when executedby a machine, cause the machine to perform any steps of the methodaccording to the first aspect or the second aspect.

This Summary is provided to introduce the selection of concepts in asimplified form, which will be further described in the DetailedDescription below. The Summary is neither intended to identify keyfeatures or main features of the present disclosure, nor intended tolimit the scope of the present disclosure.

BRIEF DESCRIPTION OF THE DRAWINGS

By describing example embodiments of the present disclosure in moredetail with reference to the accompanying drawings, the above and otherobjectives, features, and advantages of the present disclosure willbecome more apparent, wherein identical or similar reference numbersgenerally represent identical or similar components in the exampleembodiments of the present disclosure. In the drawings:

FIG. 1 is a schematic diagram of an example environment according to anembodiment of the present disclosure;

FIG. 2 is a schematic diagram of interaction of an edge computing nodewith a server and a field device according to an embodiment of thepresent disclosure;

FIG. 3 is a flow chart of a process for data processing according to anembodiment of the present disclosure;

FIG. 4 is a flow chart of a process for data processing according to anembodiment of the present disclosure;

FIG. 5 is a high-level pipeline diagram of a process for data processingaccording to an embodiment of the present disclosure; and

FIG. 6 is a block diagram of an example device that may be configured toimplement an embodiment of the present disclosure.

DETAILED DESCRIPTION

The principles of the present disclosure will be described below withreference to several example embodiments illustrated in the accompanyingdrawings.

As used herein, the term “include” and variations thereof meanopen-ended inclusion, that is, “including but not limited to.” Unlessspecifically stated, the term “or” means “and/or.” The term “based on”means “based at least in part on.” The terms “an example embodiment” and“an embodiment” indicate “a group of example embodiments.” The term“another embodiment” indicates “a group of other embodiments.” The terms“first,” “second,” and the like may refer to different or identicalobjects. Other explicit and implicit definitions may also be includedbelow.

In addition, the term “unstructured data” mentioned herein is used forrepresenting data or resources with an irregular or incomplete datastructure, having no predefined data model, and not easily representedby a two-dimensional logical table of a database, such as images andaudio/video information. The term “metadata” mentioned herein is usedfor representing structured data corresponding to unstructured data. Inaddition, the term “field device” mentioned herein is used forrepresenting a device that acquires relevant sensing information ormonitoring data on the site in real time, such as a roadside device in avehicle network, which is generally arranged close to an edge computingnode but away from a server such as a data center. Embodiments of thepresent disclosure are described below by taking the roadside device asan example, which is not intended to limit the protection scope of thepresent disclosure.

As discussed above, at present, for the unstructured data stored at theserver or the edge node, directly processing, such as searching, thedata can be performed only manually, which is time-consuming andlaborious.

For example, as unstructured data, video data for monitoring roadcondition information acquired by the roadside device cannot be searchedaccording to content. Therefore, in order to search for a vehicle orpedestrian with a certain feature or some features, a large amount ofmanpower is generally required to search the stored video data manually.It is understandable that this manner of searching is time-consuming andlaborious, and has false or missed detection problems caused by humanerror.

In addition, current roadside devices may generally upload acquiredunstructured data to a server such as a data center periodically.Therefore, the server may generally process the unstructured data whilemaintaining the unstructured data acquired by each roadside device.Since the roadside devices are generally arranged as principle servers,the acquired unstructured data is not processed in a timely manner, andthe computing load of the server may increase significantly with anincrease in the number of the roadside devices.

In order to solve, at least in part, the above problems, an embodimentof the present disclosure provides a novel solution for data processing.Firstly, a data conversion strategy may be predetermined to convertunstructured data into structured data. For example, one or a pluralityof features of a monitored object in each frame of image in a monitoringvideo may be determined by an image recognition technology, and thefeatures may be recorded as structured data in a form of coding.Secondly, a processing workload of unstructured data received in realtime may be transferred to an edge computing node, thereby reducing theworkload on a server. Structured data generated at the edge computingnode can be transmitted to the server, so as to be combined withstructured data on the side of the server. Through the above operations,the unstructured data may be processed into structured data in real timeat the edge computing node, so as to meet user requirements such assearching in a timely and effective manner.

FIG. 1 is a schematic diagram of an example environment according to anembodiment of the present disclosure. In example environment 100, adevice and/or a process according to embodiments of the presentdisclosure may be implemented. As shown in FIG. 1 , example environment100 may include server 110, edge computing nodes 121, 122, and 123(which may generally be collectively referred to as “edge computing node120” hereinafter), roadside device 130, and monitored object 141.

In FIG. 1 , server 110 is configured to manage edge computing nodes 121,122, and 123. Specifically, server 110 may send instructions to edgecomputing nodes 121, 122, and 123 to acquire monitoring data stored atedge computing nodes 121, 122, and 123. In addition, server 110 may senda data conversion strategy to edge computing nodes 121, 122, and 123 toenable edge computing nodes 121, 122, and 123 to have a function ofconverting unstructured data such as the monitoring data into structureddata.

It should be understood that, although FIG. 1 shows a situation whereedge computing nodes 121, 122, and 123 serve as nodes of the same level,in fact, various nodes in edge computing nodes 120 may have morehierarchical management relationships. That is, another edge computingnode that manages edge computing node 121 or another edge computing nodethat is managed by edge computing node 121 may exist. In addition, asshown in FIG. 1 , edge computing node 120 may communicate with one or aplurality of roadside devices 130, so as to acquire on-site roadcondition information. It should be understood that the establishment ofthe edge computing nodes can provide the roadside devices on the sitewith edge computing nodes within a short distance, thereby reducing thelatency of a communication link.

In addition, in FIG. 1 , monitored object 141 may be a vehicle. In amonitored road surface environment in FIG. 1 , monitored object 141travels in the arrow direction and may appear at position 142 asexpected. Therefore, a plurality of roadside devices 130 may all acquirea monitoring video of monitored object 141.

In some embodiments, server 110 may be any device with a computingcapability. As a non-limiting example, server 110 may be any type offixed computing device or mobile computing device, including but notlimited to a desktop computer, a laptop computer, a notebook computer, atablet computer, and the like. All or part of the components of server110 may be distributed in a cloud. Server 110 and the edge computingnodes connected thereto may also adopt a cloud-edge architecture.

In some embodiments, edge computing node 120 may include a memory atleast used for storing processed structured data. These memories may bereplaced by various other types of devices with a storage function,including but not limited to a hard disk drive (HDD), a solid statedrive (SSD), a removable disk, any other magnetic storage device and anyother optical storage device, or any combination thereof

In some embodiments, monitored object 141 may also be a pedestrian,video data uploaded by a network user, or the like.

A specific arrangement of edge computing node 220, server 210, and fielddevice 230 will be described in detail below with reference to FIG. 2 .FIG. 2 is a schematic diagram 200 of interaction of edge computing node220 with server 210 and field device 230 according to an embodiment ofthe present disclosure. As shown in FIG. 2 , server 210 may beconfigured to deliver a predetermined data conversion strategy to edgecomputing node 220. Computing device 221 included in edge computing node220 may process unstructured data from the field device 230 in real timebased on the received data conversion strategy, and store structureddata after conversion into memory 222 included in edge computing node220. It should be understood that computing device 221 may periodicallyinstruct memory 222 to upload the structured data stored therein.Alternatively or additionally, computing device 221 may also instruct,based on a data pull instruction of server 210, memory 222 to upload thestructured data stored therein. In this way, the unstructured data canbe converted into structured data at the edge computing node, so that auser can retrieve the structured data, and the computing workload on theside of the server can also be reduced.

It should be understood that, although server 210 shown in FIG. 2 isconfigured to deliver the predetermined data conversion strategy to edgecomputing node 220 and then data is processed at edge computing node220, in fact, static data stored in server 210 may also be processed atserver 210 based on the predetermined data conversion strategy. Inaddition, although only a communication connection between server 210and one edge computing node 220 is shown, communication connectionsbetween server 210 and other edge computing nodes may also be included.Accordingly, FIG. 2 is intended only to illustrate some concepts of thepresent disclosure and is not intended to limit the scope of the presentdisclosure.

A process for data processing according to embodiments of the presentdisclosure will be described in detail below with reference to FIG. 3and FIG. 4 . For ease of understanding, the specific data mentioned inthe following description are all illustrative and are not intended tolimit the scope of protection of the present disclosure. It should beunderstood that the embodiment described below may also includeadditional actions not shown and/or may omit actions shown, and thescope of the present disclosure is not limited in this regard.

FIG. 3 is a flow chart of process 300 for data processing according toan embodiment of the present disclosure. In some embodiments, process300 may be implemented in computing device 221 in FIG. 2 . Process 300for data processing according to an embodiment of the present disclosureis now described with reference to FIG. 3 . For ease of understanding,specific examples mentioned in the following description are allillustrative and are not intended to limit the protection scope of thepresent disclosure.

As shown in FIG. 3 , in 302, computing device 221 may receive a dataconversion strategy from server 210. It should be understood that thedata conversion strategy may be a data processing rule predetermined byan operator. As an example, the data conversion strategy may bedetermined based on the following JSON format.

  {    Updated: xxx,    Source: {       Filename: xxx,       Datalocation: xxx,    },    Rules: [          {             Rule: licenseplate,             Properties: [                }                  Timestamp: xxx,                   License platenumber: A-aaaaa,                   Location: {xxx, xxx},               },                {                   Timestamp: xxx,                  License plate number: A-xxxxx,                  Location: {xxx, xxx},                },             ],         },          {             Rule: human appearance,            Properties: [                {                   Gender:xxx,                   Timestamp: xxx,                   Characteristic:{ },                }             ]          }, ...       ], }

As described above, from unstructured data such as a video or an image,a file name of the video or image, a location from which data isacquired, and information such as when a license plate appears at aposition, as well as information such as the gender of a pedestrian andthe time when the pedestrian is being monitored, may be determined basedon the data conversion strategy. It should be understood that computingdevice 221 may acquire information of a monitored object in the videobased on an image recognition technology and convert, in a specificformat such as JSON, the video information into structured data that iseasy to retrieve.

In 304, computing device 221 may detect whether unstructured data fromfield device 230 is received. If computing device 221 receives theunstructured data from field device 230, the process proceeds to 306.

In 306, computing device 221 may determine metadata of the unstructureddata based on the received data conversion strategy to form a set ofmetadata. It should be understood that the metadata is structured data,which may be generated based on the JSON format above.

In some embodiments, field device 230 may be a roadside monitoringdevice, and the unstructured data is a roadside monitoring video. Inorder to determine the metadata of the unstructured data as the roadsidemonitoring video, field device 230 may identify one or a plurality offeatures of a monitored object from at least one monitoring image of theroadside monitoring video and determine the one or plurality of featuresas the metadata. As an example, when the at least one monitoring imageincludes a front image of a vehicle, computing device 221 may use animage recognition model to determine a license plate number of thevehicle in the monitoring image. In this way, the time when the vehicleusing the license plate number passes through the location of theroadside device that acquires the monitoring image may be determined. Asanother example, when the at least one monitoring image includes animage of a pedestrian, computing device 221 may use the imagerecognition model to determine the gender, hair color, height, and otherfeatures of the pedestrian. In this way, the time when the pedestrianwith the features passes through the location of the roadside devicethat acquires the monitoring image may be determined.

In 308, computing device 221 may transmit at least a part of the set ofmetadata to server 210. In some embodiments, computing device 221 maystore the processed set of metadata into memory 222 included in edgecomputing node 220. The metadata stored in memory 222 may beperiodically uploaded to server 210. Alternatively or additionally, themetadata stored in memory 222 may be uploaded to server 210 based on aninstruction of server 210. In addition, server 210 may specify a timeperiod of the metadata in the instruction, so that metadatacorresponding to unstructured data acquired during a certain period oftime can be uploaded.

In some embodiments, the at least a part of metadata transmitted toserver 210 is combined with metadata determined at server 210 ormetadata from another edge node received by server 210 as a dataprocessing result. It should be understood that, in a vehicle monitoringscenario, the data processing result may correspond to a historicaltrack of the vehicle with the license plate.

In some embodiments, process 300 may further include: determining, bycomputing device 221 when receiving a search request from server 210,target metadata matching keyword information included in the searchrequest from the set of metadata, generating a search responsecorresponding to the search request based on the target metadata, andtransmitting the search response to server 210. As an example, a usermay send a search request for searching for a license plate to server210. The search request may include a search keyword, such as licenseplate number A-aaaaa. Server 210 may deliver the search request to aplurality of edge computing nodes including edge computing node 220, andcomputing device 221 may search memory 222 based on license plate numberA-aaaaa, so as to find target metadata matching the license plate numberand then find a historical track of the license plate number. It shouldbe understood that server 210 may also search the data processing resultafter the combination for the target metadata matching the license platenumber.

It should be understood that, in the above embodiment, edge computing isperformed on unstructured data by receiving a data conversion strategyon the side of computing device 221, converting the unstructured datareceived in real time into structured data, and transmitting thestructured data back to server 210, which not only enables timelyprocessing of monitoring data, but also reduces computing load on theside of the server, thereby improving the user experience.

FIG. 4 is a flow chart of process 400 for data processing according toan embodiment of the present disclosure. In some embodiments, process400 may be implemented in server 210 in FIG. 2 . Process 400 for dataprocessing according to an embodiment of the present disclosure is nowdescribed with reference to FIG. 4 . For ease of understanding, specificexamples mentioned in the following description are all illustrative andare not intended to limit the protection scope of the presentdisclosure.

As shown in FIG. 4 , in 402, server 210 may transmit a data conversionstrategy to one or a plurality of edge computing nodes, the dataconversion strategy being used for converting unstructured data intometadata. It should be understood that the data conversion strategy isidentical with or similar to the data conversion strategy discussed inFIG. 3 , and is therefore not further described here.

In 404, server 210 may receive, from the one or plurality of edgecomputing nodes, a set of metadata determined based on the dataconversion strategy. It should be understood that the metadata isstructured data, which may be generated based on the JSON format above.

In 406, server 210 may determine a data processing result at least basedon the received set of metadata. In addition, in some embodiments,server 210 may also determine metadata of local static data based on thedata conversion strategy. As an example, server 210 may continuouslyperform the operation of determining metadata of local static data basedon the data conversion strategy when delivering the data conversionstrategy to the edge computing node.

In some embodiments, in order to determine the above data processingresult, server 210 may combine the set of metadata with the determinedmetadata of the local static data as the data processing result. Inother words, server 210 may aggregate metadata processed by the serveras well as the metadata uploaded by another edge computing node. Theaggregated metadata may provide support for a comprehensive searchfunction.

In some embodiments, the unstructured data may be a roadside monitoringvideo. In order to determine the metadata of the local static data,server 210 may identify one or a plurality of features of the monitoredobject from at least one monitoring image of the roadside monitoringvideo and determine the one or plurality of features as the metadata ofthe local static data. The metadata of the local static data may bestructured data. As an example, when the at least one monitoring imageincludes a front image of a vehicle, server 210 may use an imagerecognition model to determine a license plate number of the vehicle inthe monitoring image. In this way, the time when the vehicle using thelicense plate number passes through the location of the roadside devicethat acquires the monitoring image may be determined. As anotherexample, when the at least one monitoring image includes an image of apedestrian, server 210 may use the image recognition model to determinethe gender, hair color, height, and other features of the pedestrian. Inthis way, the time when the pedestrian with the features passes throughthe location of the roadside device that acquires the monitoring imagemay be determined.

In some embodiments, process 400 may further include: determining, byserver 210 when receiving a search request, target metadata matchingkeyword information included in the search request from the dataprocessing result, and generating a search response corresponding to thesearch request based on the target metadata. Then, server 210 maydetermine a movement track of the monitored object based on the dataprocessing result. As an example, a user may send a search request forsearching for a license plate to server 210. The search request mayinclude a search keyword, such as license plate number A-aaaaa Server210 may conduct a search based on license plate number A-aaaaa, so as tofind target metadata matching the license plate number and then find ahistorical track of the license plate number.

It should be understood that, in the above embodiment, edge processingis performed on unstructured data by generating and delivering a dataconversion strategy on the side of server 210 and aggregating the edgecomputing nodes, which not only enables timely processing of monitoringdata, but also reduces computing load on the side of the server, therebyimproving the user experience.

In order to illustrate an embodiment of the present disclosure moreclearly, FIG. 5 is a high-level pipeline diagram of process 500 for dataprocessing according to an embodiment of the present disclosure.

As shown in FIG. 5 , process 500 for data processing mainly involvesmulti-party information interaction between server 110, edge computingnode 121, edge computing node 122, roadside device 131, and roadsidedevice 132. It should be understood that FIG. 5 is only an example, andmore edge computing nodes and roadside devices may be included.

In FIG. 5 , process 500 for data processing generally starts from server110. A data conversion strategy determined by a user may generally begenerated on the side of server 110, and the data conversion strategy issent (501) to edge computing node 121 and sent (502) to edge computingnode 122. Roadside device 131 may be configured to transmit (503)unstructured data such as a monitoring video to edge computing node 121in real time, and roadside device 132 may be configured to transmit(504) unstructured data such as a monitoring video to edge computingnode 122 in real time. It should be understood that the two transmission(503, 504) processes may be performed at different times.

Then, edge computing node 121 performs data processing (505) on theunstructured data from roadside device 131 based on the received dataconversion strategy to convert the unstructured data into structureddata or metadata. In addition, edge computing node 122 performs dataprocessing (506) on the unstructured data from roadside device 132 basedon the received data conversion strategy to convert the unstructureddata into structured data or metadata. It should be understood that thedata processing (505, 506) processes may be performed at differenttimes. Further, edge computing node 121 transmits (507) the structureddata after conversion to server 110, and edge computing node 122transmits (508) the structured data after conversion to server 110.

It should be understood that, after server 110 generates the dataconversion strategy, server 110 may perform data processing (509) onstatic data stored locally in any time period, that is, converthistorical data stored at server 110 as unstructured data intostructured data. Moreover, server 110 may combine (510) local structureddata with structured data from edge computing node 121 and structureddata from edge computing node 122 to generate a data processing result.

In this way, server 110 may use a plurality of edge computing nodes tocomprehensively acquire structured data of all monitored objects in amonitoring region, which is conducive to searching, monitoring, movementtrack generation, and other operations.

FIG. 6 is a block diagram of example electronic device 600 that may beconfigured to implement an embodiment of the present disclosure. Forexample, device 600 may be configured to implement computing device 221as shown in FIG. 2 . As shown in the drawing, device 600 includes acentral processing unit (CPU) 601 that may perform various appropriateactions and processing according to computer program instructions storedin read-only memory (ROM) 602 or computer program instructions loadedfrom storage unit 608 into random access memory (RAM) 603. Variousprograms and data required for the operation of device 600 may also bestored in RAM 603. CPU 601, ROM 602, and RAM 603 are connected to eachother through bus 604. Input/output (I/O) interface 605 is alsoconnected to bus 604.

A plurality of components in device 600 are connected to I/O interface605, including: input unit 606, such as a keyboard and a mouse; outputunit 607, such as various types of displays and speakers; storage unit608, such as a magnetic disk and an optical disc; and communication unit609, such as a network card, a modem, and a wireless communicationtransceiver. Communication unit 609 allows device 600 to exchangeinformation/data with other devices via a computer network, such as theInternet, and/or various telecommunication networks.

CPU 601 performs the various methods and processing described above,such as processes 300 and 400. For example, in some embodiments, thevarious methods and processing described above may be implemented as acomputer software program or a computer program product, which istangibly included in a machine-readable medium, such as storage unit608. In some embodiments, part of or all the computer program may beloaded and/or installed onto device 600 via ROM 602 and/or communicationunit 609. When the computer program is loaded into RAM 603 and executedby CPU 601, one or a plurality of steps of any process described abovemay be implemented. Alternatively, in other embodiments, CPU 601 may beconfigured in any other suitable manners (for example, by means offirmware) to perform a process such as processes 300 and 400.

Embodiments of the present disclosure include a method, an apparatus, asystem, and/or a computer program product. The computer program productmay include a computer-readable storage medium on whichcomputer-readable program instructions for performing various aspects ofthe present disclosure are loaded.

The computer-readable storage medium may be a tangible device that mayretain and store instructions used by an instruction-executing device.For example, the computer-readable storage medium may be, but is notlimited to, an electrical storage device, a magnetic storage device, anoptical storage device, an electromagnetic storage device, asemiconductor storage device, any non-transitory storage device, or anyappropriate combination of those described above. More specific examples(a non-exhaustive list) of the computer-readable storage medium include:a portable computer disk, a hard disk, a RAM, a ROM, an erasableprogrammable read-only memory (EPROM or flash memory), a static randomaccess memory (SRAM), a portable compact disc read-only memory (CD-ROM),a digital versatile disc (DVD), a memory stick, a floppy disk, amechanical encoding device, for example, a punch card or a raisedstructure in a groove with instructions stored thereon, and any suitablecombination of the foregoing. The computer-readable storage medium usedherein is not to be interpreted as transient signals per se, such asradio waves or other freely propagating electromagnetic waves,electromagnetic waves propagating through waveguides or othertransmission media (e.g., light pulses through fiber-optic cables), orelectrical signals transmitted through electrical wires.

The computer-readable program instructions described herein may bedownloaded from a computer-readable storage medium to variouscomputing/processing devices or downloaded to an external computer orexternal storage device via a network, such as the Internet, a localarea network, a wide area network, and/or a wireless network. Thenetwork may include copper transmission cables, fiber optictransmission, wireless transmission, routers, firewalls, switches,gateway computers, and/or edge servers. A network adapter card ornetwork interface in each computing/processing device receivescomputer-readable program instructions from a network and forwards thecomputer-readable program instructions for storage in acomputer-readable storage medium in the computing/processing device.

The computer program instructions for executing the operation of someembodiments of the present disclosure may be assembly instructions,instruction set architecture (ISA) instructions, machine instructions,machine-dependent instructions, microcode, firmware instructions, statussetting data, or source code or object code written in any combinationof one or a plurality of programming languages, the programminglanguages including object-oriented programming languages such asSmalltalk and C++, and conventional procedural programming languagessuch as the C language or similar programming languages. Thecomputer-readable program instructions may be executed entirely on auser computer, partly on a user computer, as a stand-alone softwarepackage, partly on a user computer and partly on a remote computer, orentirely on a remote computer or a server. In a case where a remotecomputer is involved, the remote computer may be connected to a usercomputer through any kind of networks, including a local area network(LAN) or a wide area network (WAN), or may be connected to an externalcomputer (for example, connected through the Internet using an Internetservice provider). In some embodiments, an electronic circuit, such as aprogrammable logic circuit, a field programmable gate array (FPGA), or aprogrammable logic array (PLA), is customized by utilizing statusinformation of the computer-readable program instructions. Theelectronic circuit may execute the computer-readable programinstructions to implement various aspects of the present disclosure.

Various aspects of the present disclosure are described herein withreference to flow charts and/or block diagrams of the method, theapparatus (system), and the computer program product according toembodiments of the present disclosure. It should be understood that eachblock of the flow charts and/or the block diagrams and combinations ofblocks in the flow charts and/or the block diagrams may be implementedby computer-readable program instructions.

These computer-readable program instructions may be provided to aprocessing unit of a general-purpose computer, a special-purposecomputer, or a further programmable data processing apparatus, therebyproducing a machine, such that these instructions, when executed by theprocessing unit of the computer or the further programmable dataprocessing apparatus, produce means for implementing functions/actionsspecified in one or a plurality of blocks in the flow charts and/orblock diagrams. These computer-readable program instructions may also bestored in a computer-readable storage medium, and these instructionscause a computer, a programmable data processing apparatus, and/or otherdevices to operate in a specific manner; and thus the computer-readablemedium having instructions stored includes an article of manufacturethat includes instructions that implement various aspects of thefunctions/actions specified in one or a plurality of blocks in the flowcharts and/or block diagrams.

The computer-readable program instructions may also be loaded to acomputer, a further programmable data processing apparatus, or a furtherdevice, so that a series of operating steps may be performed on thecomputer, the further programmable data processing apparatus, or thefurther device to produce a computer-implemented process, such that theinstructions executed on the computer, the further programmable dataprocessing apparatus, or the further device may implement thefunctions/actions specified in one or a plurality of blocks in the flowcharts and/or block diagrams.

The flow charts and block diagrams in the drawings illustrate thearchitectures, functions, and operations of possible embodiments of thesystems, methods, and computer program products according to variousembodiments of the present disclosure. In this regard, each block in theflow charts or block diagrams may represent a module, a program segment,or part of an instruction, the module, program segment, or part of aninstruction including one or a plurality of executable instructions forimplementing specified logical functions. In some alternativeembodiments, functions marked in the blocks may also occur in an orderdifferent from that marked in the accompanying drawings. For example,two successive blocks may actually be executed in parallelsubstantially, and sometimes they may also be executed in a reverseorder, which depends on involved functions. It should be further notedthat each block in the block diagrams and/or flow charts as well as acombination of blocks in the block diagrams and/or flow charts may beimplemented by using a special hardware-based system that executesspecified functions or actions, or implemented by using a combination ofspecial hardware and computer instructions.

Various embodiments of the present disclosure have been described above.The foregoing description is illustrative rather than exhaustive, and isnot limited to the disclosed embodiments. Numerous modifications andalterations will be apparent to persons of ordinary skill in the artwithout departing from the scope and spirit of the illustratedembodiments. The selection of terms used herein is intended to bestexplain the principles and practical applications of the embodiments orthe improvements to technologies on the market, so as to enable personsof ordinary skill in the art to understand the embodiments disclosedherein.

What is claimed is:
 1. A method for data processing, comprising:receiving a data conversion strategy from a server; determining, inresponse to receiving unstructured data from a field device, metadata ofthe unstructured data based on the received data conversion strategy toform a set of metadata; and transmitting at least a part of the set ofmetadata to the server.
 2. The method according to claim 1, furthercomprising: determining, in response to receiving a search request fromthe server, target metadata matching keyword information comprised inthe search request from the set of metadata; generating a searchresponse corresponding to the search request based on the targetmetadata; and transmitting the search response to the server.
 3. Themethod according to claim 1, wherein the at least a part of metadatatransmitted to the server is combined with metadata determined at theserver or metadata from another edge node received by the server as adata processing result.
 4. The method according to claim 1, wherein thefield device is a roadside monitoring device, and the unstructured datais a roadside monitoring video, and wherein determining metadata of theunstructured data comprises: identifying one or a plurality of featuresof a monitored object from at least one monitoring image of the roadsidemonitoring video; and determining the one or plurality of features asthe metadata, the metadata being structured data.
 5. A method for dataprocessing, comprising: transmitting a data conversion strategy to oneor a plurality of edge computing nodes, the data conversion strategybeing used for converting unstructured data into metadata; receiving,from the one or plurality of edge computing nodes, a set of metadatadetermined based on the data conversion strategy; and determining a dataprocessing result at least based on the received set of metadata.
 6. Themethod according to claim 5, further comprising: determining metadata oflocal static data based on the data conversion strategy, whereindetermining the data processing result comprises: combining the set withthe determined metadata of the local static data as the data processingresult.
 7. The method according to claim 5, further comprising:determining, in response to receiving a search request, target metadatamatching keyword information comprised in the search request from thedata processing result; and generating a search response correspondingto the search request based on the target metadata.
 8. The methodaccording to claim 6, wherein the unstructured data is a roadsidemonitoring video, and wherein determining metadata of the local staticdata comprises: identifying one or a plurality of features of amonitored object from at least one monitoring image of the roadsidemonitoring video; and determining the one or plurality of features asthe metadata of the local static data, the metadata of the local staticdata being structured data.
 9. The method according to claim 8, furthercomprising: determining a movement track of the monitored object basedon the data processing result.
 10. An apparatus comprising: anelectronic device, wherein the electronic device comprises: a processor;and a memory, coupled to the processor and having instructions storedtherein, wherein the instructions, when executed by the processor, causethe electronic device to perform actions comprising: receiving a dataconversion strategy from a server; determining, in response to receivingunstructured data from a field device, metadata of the unstructured databased on the received data conversion strategy to form a set ofmetadata; and transmitting at least a part of the set of metadata to theserver.
 11. The apparatus according to claim 10, wherein the actionsfurther comprise: determining, in response to receiving a search requestfrom the server, target metadata matching keyword information comprisedin the search request from the set of metadata; generating a searchresponse corresponding to the search request based on the targetmetadata; and transmitting the search response to the server.
 12. Theapparatus according to claim 10, wherein the at least a part of metadatatransmitted to the server is combined with metadata determined at theserver or metadata from another edge node received by the server as adata processing result.
 13. The apparatus according to claim 10, whereinthe field device is a roadside monitoring device, and the unstructureddata is a roadside monitoring video, and wherein determining metadata ofthe unstructured data comprises: identifying one or a plurality offeatures of a monitored object from at least one monitoring image of theroadside monitoring video; and determining the one or plurality offeatures as the metadata, the metadata being structured data.
 14. Theapparatus according to claim 10, further comprising: an additionalelectronic device, comprising: a processor; and a memory, coupled to theprocessor and having instructions stored therein, wherein theinstructions, when executed by the processor, cause the additionalelectronic device to perform actions comprising: transmitting a dataconversion strategy to one or a plurality of edge computing nodes, thedata conversion strategy being used for converting unstructured datainto metadata; receiving, from the one or plurality of edge computingnodes, a set of metadata determined based on the data conversionstrategy; and determining a data processing result at least based on thereceived set of metadata.
 15. The apparatus according to claim 14,wherein the actions performed by the additional electronic devicefurther comprise: determining metadata of local static data based on thedata conversion strategy, wherein determining the data processing resultcomprises: combining the set with the determined metadata of the localstatic data as the data processing result.
 16. The apparatus accordingto claim 14, wherein the actions performed by the additional electronicdevice further comprise: determining, in response to receiving a searchrequest, target metadata matching keyword information comprised in thesearch request from the data processing result; and generating a searchresponse corresponding to the search request based on the targetmetadata.
 17. The apparatus according to claim 15, wherein theunstructured data is a roadside monitoring video, and whereindetermining metadata of the local static data comprises: identifying oneor a plurality of features of a monitored object from at least onemonitoring image of the roadside monitoring video; and determining theone or plurality of features as the metadata of the local static data,the metadata of the local static data being structured data.
 18. Theapparatus according to claim 17, wherein the actions performed by theadditional electronic device further comprise: determining a movementtrack of the monitored object based on the data processing result.
 19. Acomputer program product tangibly stored on a non-transitorycomputer-readable medium and comprising machine-executable instructions,wherein the machine-executable instructions, when executed by a machine,cause the machine to perform the method of claim
 1. 20. A computerprogram product tangibly stored on a non-transitory computer-readablemedium and comprising machine-executable instructions, wherein themachine-executable instructions, when executed by a machine, cause themachine to perform the method of claim 5.